Human rating methods on multi-agent systems

Abstract

Modern artificial intelligence approaches study game-playing agents in multi-agent social environments, in order to better simulate the real world playing behaviors; these approaches have already produced promising results. In this paper we present the results of applying human rating systems for competitive games with social activity, to evaluate synthetic agents’ performance in multi-agent systems. The widely used Elo and Glicko rating systems are tested in large-scale synthetic multi-agent game-playing social events, and their rating outcome is presented and analyzed.